{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using matplotlib to Visualize Stackdriver Monitoring Metrics for Cloud Bigtable\n",
    "\n",
    "This tutorial demonstrates how to use the Python [matplotlib](https://matplotlib.org/) library to visualize Cloud Bigtable metrics with [Stackdriver Monitoring](https://cloud.google.com/monitoring/). While you can visualize some metrics in the Cloud Bigtable console or in the Stackriver Monitoring console using `Metrics Explorer`, using `matplotlib` offers you the full flexibility and expressiveness of Python and the [pandas](http://pandas.pydata.org/) data science library and a powerful toolkit for designing custom graphs and figures.\n",
    "\n",
    "`matplotlib` and `pandas` can be used with Stackdriver Monitoring with [Jupyter](http://jupyter.org/) notebooks, or with [Google Cloud Datalab](https://cloud.google.com/datalab/). Follow this link for [a more in-depth tutorial of the Stackdriver Monitoring pandas integration with Cloud Datalab.](https://github.com/googledatalab/notebooks/blob/master/tutorials/Stackdriver%20Monitoring/Getting%20started.ipynb).\n",
    "\n",
    "## Dependencies\n",
    "\n",
    "This notebook depends on `matplotlib`, `pandas`, and the [google-cloud-monitoring](https://github.com/GoogleCloudPlatform/google-cloud-python/tree/master/monitoring) Python client library. These are also listed in `requirements.txt`.\n",
    "\n",
    "## Cloud Bigtable autoscaler\n",
    "\n",
    "This tutorial demonstrates metrics obtained from running a Cloud Bigtable [loadtest](https://github.com/GoogleCloudPlatform/google-cloud-go/blob/master/bigtable/cmd/loadtest/loadtest.go), while also \n",
    "running the [Bigtable autoscaler sample](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/master/bigtable/autoscaler). Notably, we plot several Cloud Bigtable metrics such as CPU load and bytes received, and examine how they change in response to a change in node counts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import datetime\n",
    "\n",
    "from google.cloud import monitoring\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our necessary libraries imported, we first gather the relevant metrics. In this case, a load test was performed for an hour that was finished on June 6, 2017. We query for several metrics that ended at that last hour, and convert them to pandas dataframes. To simply query for recent metrics, omit the `end_time` parameter.\n",
    "\n",
    "One useful gotcha to note is that a mispelled metric name in the `query` call will result in a \"a 403 - user does not have permission to see metric\", which might mislead you into thinking there was an authentication error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "client = monitoring.Client()\n",
    "\n",
    "# Replace this timestamp with one where you expect to see Bigtble metrics\n",
    "end_time = datetime.datetime(year=2017, month=6, day=12, hour=23)\n",
    "\n",
    "# Queries from end_time - 1 hour until end_time\n",
    "# end_time defaults to now, so omit it to get recent metrics\n",
    "node_count_query = client.query('bigtable.googleapis.com/cluster/node_count', end_time=end_time, hours=1)\n",
    "cpu_query = client.query('bigtable.googleapis.com/cluster/cpu_load', end_time=end_time, hours=1)\n",
    "received_bytes_query = client.query('bigtable.googleapis.com/server/received_bytes_count', end_time=end_time, hours=1)\n",
    "latency_query = client.query('bigtable.googleapis.com/server/latencies', end_time=end_time, hours=1)\n",
    "\n",
    "# Align the queries along the same time intervals\n",
    "node_count_query = node_count_query.align(monitoring.Aligner.ALIGN_MEAN, minutes=5)\n",
    "cpu_query = cpu_query.align(monitoring.Aligner.ALIGN_MEAN, minutes=5)\n",
    "received_bytes_query = received_bytes_query.align(monitoring.Aligner.ALIGN_MEAN, minutes=5)\n",
    "latency_query = received_bytes_query.align(monitoring.Aligner.ALIGN_MEAN, minutes=5)\n",
    "\n",
    "# Gather the queries as Pandas dataframes\n",
    "# This will make the actual API call\n",
    "node_count_df = node_count_query.as_dataframe()\n",
    "cpu_df = cpu_query.as_dataframe()\n",
    "\n",
    "# Align the received bytes query along 5 minutes to reduce noise\n",
    "received_bytes_query = received_bytes_query.align(monitoring.Aligner.ALIGN_MEAN, minutes=5)\n",
    "received_bytes_df = received_bytes_query.as_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our data gathered, we can now plot it using `matplotlib`. In this case, we simply create multiple graphs on top of each other. Aligned vertically, we can see that as node count increased, metrics ilke average CPU usage decreased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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X8CkBiMgsEdkuIiUicncr2+NF5GXP9g9EJMvfgRpjut7cqdkcOHaa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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e6d190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1116e8290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rc1KpXryX5h2VbYoqoAI8t+s5xseP56LRF/VL/laUz4f3yJG2t3l3SQme4hI8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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d30e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('CPU usage over time')\n",
    "plt.plot(cpu_df)\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Cloud Bigtable node count over time')\n",
    "plt.plot(node_count_df)\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Bytes received by the Cloud Bigtable server over time')\n",
    "plt.plot(received_bytes_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
